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Model: Rumiii/Qwen2.5-0.5B-Medical-ReasonMed370K Source: Original Platform
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README.md
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---
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license: apache-2.0
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datasets:
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- lingshu-medical-mllm/ReasonMed
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base_model:
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- unsloth/Qwen2.5-0.5B-Instruct
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---
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## Info
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# Qwen2.5-0.5B-Medical-ReasonMed370K
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A 0.5 billion parameter medical reasoning model fine-tuned on the complete ReasonMed 370K dataset. This model is built on top of Qwen2.5-0.5B-Instruct and trained to perform structured clinical reasoning, differential diagnosis, and evidence-based medical question answering.
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## Model Details
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- **Base Model**: unsloth/Qwen2.5-0.5B-Instruct
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- **Model Size**: 0.5B parameters
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- **Fine-tuning Method**: LoRA via Unsloth
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- **Training Dataset**: ReasonMed 370K (full dataset)
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- **Training Hardware**: NVIDIA Tesla T4 (Kaggle free tier)
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- **License**: Apache 2.0
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## Training Details
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The model was fine-tuned in two stages, each covering half of the ReasonMed dataset:
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**Stage 1**: Fine-tuned on the first 185,000 samples of ReasonMed using LoRA with the following configuration:
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- LoRA rank: 8
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- LoRA alpha: 16
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- Learning rate: 5e-5
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- Batch size: 2 with 16 gradient accumulation steps
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- Max sequence length: 4096
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- Epochs: 1
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- Optimizer: AdamW 8-bit
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**Stage 2**: Continued fine-tuning on the remaining 184,983 samples with identical configuration, completing one full pass over the entire 370K dataset.
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Both stages used `packing=False` to ensure every sample was processed individually without truncation.
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## Dataset
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This model was trained on [ReasonMed](https://huggingface.co/datasets/lingshu-medical-mllm/ReasonMed), the largest open-source medical reasoning dataset available, comprising 370,000 high-quality examples distilled from 1.75 million initial reasoning paths generated by multiple large language models.
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ReasonMed is built through a multi-agent verification and refinement pipeline that includes an Error Refiner to correct error-prone reasoning steps. Each example combines detailed chain-of-thought reasoning with a concise answer summary, covering a wide range of medical topics including clinical reasoning, differential diagnosis, pharmacology, and medical question answering.
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For more details on the dataset, refer to the official repository: https://github.com/alibaba-damo-academy/ReasonMed
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## What the Model Can Do
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After training on the full ReasonMed dataset, the model demonstrates the ability to:
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- Work through clinical presentations step by step
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- Generate differential diagnoses with reasoning for each option
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- Rule out unlikely diagnoses with justification
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- Provide structured final answers with clinical pearls
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- Reason through medical multiple choice questions with explanation
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## Demo
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The screenshot above shows the model running through a clinical scenario involving hypothyroidism, demonstrating its ability to identify key symptoms, interpret lab values, and produce a structured response with management guidance.
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## Limitations
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- This is a 0.5B parameter model and has a hard ceiling on reasoning depth and factual recall
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- Small models are prone to inconsistency across similar questions
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- The model may occasionally hallucinate clinical details
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- This model is intended for research and educational purposes only
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- It should not be used for real clinical decision making or as a substitute for a qualified medical professional
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## Usage
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```python
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from unsloth import FastLanguageModel
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import torch
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "Rumiii/Qwen2.5-0.5B-Medical-ReasonMed370K",
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max_seq_length = 4096,
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load_in_4bit = True,
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)
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FastLanguageModel.for_inference(model)
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messages = [
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{"role": "user", "content": "Your medical question here"}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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tokenize = True,
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add_generation_prompt = True,
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return_tensors = "pt"
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).to("cuda")
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outputs = model.generate(
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input_ids = inputs,
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max_new_tokens = 1024,
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temperature = 0.7,
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do_sample = True,
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repetition_penalty = 1.3,
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no_repeat_ngram_size = 3,
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top_p = 0.9,
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top_k = 50,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Citation
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If you use this model, please cite the ReasonMed dataset:
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```bibtex
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@misc{sun2025reasonmed370kmultiagentgenerated,
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title={ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning},
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author={Yu Sun and Xingyu Qian and Weiwen Xu and Hao Zhang and Chenghao Xiao and Long Li and Yu Rong and Wenbing Huang and Qifeng Bai and Tingyang Xu},
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year={2025},
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eprint={2506.09513},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2506.09513},
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}
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```
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## Acknowledgements
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Training was conducted on Kaggle free tier infrastructure using Unsloth for efficient fine-tuning. The ReasonMed dataset was created by the team at Alibaba DAMO Academy and Tencent AI Lab.
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53
chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %} {{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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58
config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"torch_dtype": "float16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 151665,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"unsloth_fixed": true,
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"unsloth_version": "2026.3.4",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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size 988097824
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version https://git-lfs.github.com/spec/v1
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oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
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size 11422356
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"is_local": true,
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"model_max_length": 32768,
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"pad_token": "<|PAD_TOKEN|>",
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"padding_side": "left",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null,
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %} {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
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}
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